Splitting trajectories¶

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Gaps are quite common in trajectories. For example, GPS tracks may contain gaps if moving objects enter tunnels where GPS reception is lost. In other use cases, moving objects may leave the observation area for longer time before returning and continuing their recorded track.

Depending on the use case, we therefore might want to split trajectories at observation gaps that exceed a certain minimum duration:

In [1]:
import pandas as pd
import geopandas as gpd
import movingpandas as mpd
import shapely as shp
import hvplot.pandas 
import matplotlib.pyplot as plt

from geopandas import GeoDataFrame, read_file
from shapely.geometry import Point, LineString, Polygon
from datetime import datetime, timedelta
from holoviews import opts

import warnings
warnings.filterwarnings('ignore')

plot_defaults = {'linewidth':5, 'capstyle':'round', 'figsize':(9,3), 'legend':True}
opts.defaults(opts.Overlay(active_tools=['wheel_zoom'], frame_width=500, frame_height=400))

mpd.show_versions()

MovingPandas 0.15.rc1

SYSTEM INFO
-----------
python     : 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:39:05) [MSC v.1929 64 bit (AMD64)]
executable : H:\miniconda3\envs\mpd-ex\python.exe
machine    : Windows-10-10.0.19045-SP0

GEOS, GDAL, PROJ INFO
---------------------
GEOS       : None
GEOS lib   : None
GDAL       : 3.5.0
GDAL data dir: None
PROJ       : 9.0.0
PROJ data dir: H:\miniconda3\pkgs\proj-9.0.0-h1cfcee9_1\Library\share\proj

PYTHON DEPENDENCIES
-------------------
geopandas  : 0.12.2
pandas     : 1.5.3
fiona      : 1.8.21
numpy      : 1.24.1
shapely    : 1.8.2
rtree      : 1.0.0
pyproj     : 3.3.1
matplotlib : 3.6.3
mapclassify: None
geopy      : 2.3.0
holoviews  : 1.14.9
hvplot     : 0.8.2
geoviews   : 1.9.6
stonesoup  : 0.1b11
In [2]:
gdf = read_file('../data/geolife_small.gpkg')
traj_collection = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
In [3]:
my_traj = traj_collection.trajectories[1]
print(my_traj)

Trajectory 2 (2009-06-29 07:02:25 to 2009-06-29 11:13:12) | Size: 897 | Length: 38764.6m
Bounds: (116.319212, 39.971703, 116.592616, 40.082514)
LINESTRING (116.590957 40.071961, 116.590905 40.072007, 116.590879 40.072027, 116.590915 40.072004,
In [4]:
my_traj.plot(column='speed', vmax=20, **plot_defaults)
Out[4]:
<AxesSubplot: >

ObservationGapSplitter¶

Split the trajectory where then are no observations for at least two minutes:

In [5]:
split = mpd.ObservationGapSplitter(my_traj).split(gap=timedelta(minutes=2))
split
Out[5]:
TrajectoryCollection with 5 trajectories
In [6]:
split.to_traj_gdf()
Out[6]:
traj_id start_t end_t geometry length direction
0 2_0 2009-06-29 07:02:25 2009-06-29 07:13:55 LINESTRING (116.59096 40.07196, 116.59091 40.0... 2367.642888 345.820618
1 2_1 2009-06-29 07:16:55 2009-06-29 07:17:05 LINESTRING (116.58690 40.07961, 116.58689 40.0... 36.188090 188.079866
2 2_2 2009-06-29 07:29:35 2009-06-29 08:20:15 LINESTRING (116.58703 40.07951, 116.58704 40.0... 33766.853732 250.896081
3 2_3 2009-06-29 10:57:17 2009-06-29 11:06:52 LINESTRING (116.31970 40.00751, 116.31971 40.0... 1613.488553 140.473858
4 2_4 2009-06-29 11:09:12 2009-06-29 11:10:07 LINESTRING (116.32636 40.00025, 116.32349 40.0... 574.481893 40.696636
In [7]:
fig, axes = plt.subplots(nrows=1, ncols=len(split), figsize=(19,4))
for i, traj in enumerate(split):
    traj.plot(ax=axes[i], linewidth=5.0, capstyle='round', column='speed', vmax=20)

StopSplitter¶

Split the trajectory where observations stay within 30 meters for at least 1 minute. Discard created trajectories that are shorter than 500 meters long:

In [8]:
split = mpd.StopSplitter(my_traj).split(max_diameter=30, min_duration=timedelta(minutes=1), min_length=500)
split
Out[8]:
TrajectoryCollection with 4 trajectories
In [9]:
split.to_traj_gdf()
Out[9]:
traj_id start_t end_t geometry length direction
0 2_2009-06-29 07:06:55 2009-06-29 07:06:55 2009-06-29 07:12:25 LINESTRING (116.59258 40.07420, 116.59254 40.0... 1846.155876 331.632126
1 2_2009-06-29 07:29:35 2009-06-29 07:29:35 2009-06-29 08:02:30 LINESTRING (116.58703 40.07951, 116.58704 40.0... 29771.464660 244.569782
2 2_2009-06-29 08:07:00 2009-06-29 08:07:00 2009-06-29 11:04:22 LINESTRING (116.32328 39.98481, 116.32345 39.9... 5012.014680 11.235899
3 2_2009-06-29 11:06:12 2009-06-29 11:06:12 2009-06-29 11:13:12 LINESTRING (116.32741 39.99990, 116.32744 39.9... 786.228144 3.232105
In [10]:
fig, axes = plt.subplots(nrows=1, ncols=len(split), figsize=(19,4))
for i, traj in enumerate(split):
    traj.plot(ax=axes[i], linewidth=5.0, capstyle='round', column='speed', vmax=20)

SpeedSplitter¶

Split the trajectory where the speed is below one meters per second for at least five minutes:

In [11]:
split = mpd.SpeedSplitter(my_traj).split(speed=1, duration=timedelta(minutes=5))
split
Out[11]:
TrajectoryCollection with 3 trajectories
In [12]:
split.to_traj_gdf()
Out[12]:
traj_id start_t end_t geometry length direction
0 2_0 2009-06-29 07:02:25 2009-06-29 07:17:05 LINESTRING (116.59096 40.07196, 116.59091 40.0... 2521.655180 336.756427
1 2_1 2009-06-29 07:29:55 2009-06-29 08:20:15 LINESTRING (116.58704 40.07947, 116.58705 40.0... 33662.308829 250.906244
2 2_2 2009-06-29 10:57:22 2009-06-29 11:10:07 LINESTRING (116.31971 40.00759, 116.31964 40.0... 2231.112048 138.778458
In [13]:
fig, axes = plt.subplots(nrows=1, ncols=len(split), figsize=(19,4))
for i, traj in enumerate(split):
    traj.plot(ax=axes[i], linewidth=5.0, capstyle='round', column='speed', vmax=20)
In [ ]: